Affiliations: [a]
Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, P. R. China
| [b] School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, P. R. China
| [c] School of Computer Science, Shaanxi Normal University, Xi’an, P. R. China
Correspondence:
[*]
Corresponding author. Hanqiang Liu, School of Computer Science, Shaanxi Normal University, Xi’an, P. R. China. E-mail: [email protected].
Abstract: Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perform multilevel thresholding under several segmentation requirements. In order to solve these problems, an adaptive multilevel thresholding algorithm based on multiobjective artificial bee colony optimization (AMT-MABCO) segmentation is presented for noisy image in this paper. To improve the robustness of AMT-MABCO to image noise, a line intercept histogram which considers both the intensity and coordinate information in the neighborhood of the pixels is firstly utilized to define a novel between-class variance function as one fitness function. Then, an interval-valued fuzzy entropy function is constructed as another fitness function to deal with the blurred characteristic in images. AMT-MABCO tries to obtain a compromising multilevel thresholding result under these two segmentation requirements. To adaptively determine the number of thresholds, a grouping population initialization and evaluation strategies are proposed in AMT-MABCO. Furthermore, two novel search equations are constructed in AMT-MABCO to generate candidate solutions in the employed bees and onlookers phases, respectively. Experimental results show that AMT-MABCO outperforms state-of-the-art thresholding methods in noise robustness and segmentation performance.
